In order to solve a trade-off problem between temperature and thermal stress of pistons for cross section design of a cooling gallery, a multi-objective optimization and a co-simulation workflow are carried out to calculate an optimal design and thermal solutions inside the piston’s thermal system of a diesel engine. Six structural parameters are selected as decision variables to describe the cross section shape of the cooling gallery. An oil filling ratio is regarded as an additional variable for finding the optimal volume fracture of the cooling gallery. The objective functions include the maximum temperature and the maximum thermal stress. In order to avoid a new gallery shape over the borders of the piston’s cross section, several constraints need to be satisfied in the optimization. Multiple tools of the optimization, including the Sobol Sequence, the Support Vector Machine for regression, a variant of the non-dominated sorting genetic algorithm II, and the k-means clustering method, are integrated together to seek the optimal solutions for the design problem. The predicted results by the SVR models agree well with those obtained by the co-simulation method with a high determination coefficient greater than 0.91. A set of Pareto optimal solutions is obtained through the evolution of 100 generations on the basis of the SVR models. The results reveal that the Pareto optimal solutions are effective due to two thermal solutions out of the three representative solutions that are partitioned by k-means clustering. The design proposal selected from the three representative solutions indicates that the optimal position of the gallery cross section should be far away from the grooves and close to the bottom of the combustion chamber and the top region of the inner chamber. The cooling oil filling ratio should be increased to approximately 50% by increasing the mass flow rate of the cooling oil or the cooling oil capture capacity of the gallery in order to satisfy the design goal of reducing piston temperature. The optimization results reflects an advance of design improvement methodology in the field of piston cooling systems.

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sobol svr models mass flow rate italickitalicmeans clustering method multiobjective optimization decision variables nondominated sorting genetic algorithm objective optimal volume fracture of the cooling tradeoff problem between temperature and thermal stress of pistons for cross section design of a cooling gallery diesel cosimulation workflow design improvement methodology support vector machine for regression cooling oil capture capacity determination coefficient pistons thermal system solutions structural parameters piston cooling systems maximum thermal stress

Jilin Lei, Lizhong Shen, Zhigao Wen, Jun Wen,Xiwen Deng,.Multi-objective optimization of cooling galleries inside pistons of a diesel engine. 132 (0),.

Jilin Lei, et al."Multi-objective optimization of cooling galleries inside pistons of a diesel engine" 132

Jilin Lei, Lizhong Shen, Zhigao Wen, Jun Wen,Xiwen Deng,"Multi-objective optimization of cooling galleries inside pistons of a diesel engine"